IDENTIFYING AREA HOTSPOTS AND TAXI PICKUP TIMES USING SPATIAL DENSITY-BASED CLUSTERING
Taxis are one of the competitive sectors of transportation and are recognized as convenient and easy means of transportation to meet individual needs. However, in the operation of a taxi there are some problems that would make the taxi service less optimal, such as the difficulty with finding a taxi at specific hours, the imbalance between demand and taxi supplies, and the length of passengers waiting for a taxi. Therefore, to optimize taxi service, a knowledge base is needed for strategic management decision making. In the study, data of exploration taxis uses a DBSCAN algorithm aimed at identifying and clustering pickup hotspots based on time during weekday and weekend time from Queens, New York City. As for the features used which are pickup latitude and pickup longitude. Accuracy scores for modeling use coefficients to achieve accuracy scores of 0.80 on weekdays and 0.77 on weekends where the accuracy score falls into the accurate category in modeling. Results show that there are three areas of taxi pickup centers based on high taxi demand in January 2016, where they are at LaGuardia airport, John f. Kennedy international, and the area around Steinway Street.
R. Rivai, “Identifikasi Perilaku Penggunaan Dan Persepsi Pengguna Tentang Layanan Pemesanan Dan Pengiriman Makanan Dengan Transportasi Online,” other, Universitas Komputer Indonesia, 2020. doi: 10/BAB%20IV%20-%20Unikom%20-%20Riyaldi%20Rivai%20-%2010615004.pdf.
A. S. Athoillah, M. Firdaus, and B. Sanim, “Competititve strategy of taxi company in facing environmental changes,” BISMA (Bisnis dan Manajemen), vol. 12, no. 1, Art. no. 1, Oct. 2019, doi: 10.26740/bisma.v12n1.p66-87.
UITP, “GLOBAL TAXI BENCHMARKING STUDY 2019,” Nov. 2020. Accessed: Dec. 06, 2022. [Online]. Available: https://cms.uitp.org/wp/wp-content/uploads/2020/11/Statistics-Brief-TAxi-Benchmarking_NOV2020-web.pdf
“Ride-hailing & Taxi - Worldwide | Statista Market Forecast,” Statista, Dec. 2022. https://www.statista.com/outlook/mmo/shared-mobility/shared-rides/ride-hailing-taxi/worldwide (accessed Mar. 14, 2023).
M. B. Ulak, A. Yazici, and M. Aljarrah, “Value of convenience for taxi trips in New York City,” Transportation Research Part A: Policy and Practice, vol. 142, pp. 85–100, Dec. 2020, doi: 10.1016/j.tra.2020.10.016.
H. Yao, Z. Huang, X. Ye, M. Grifoll, G. Liu, and P. Zheng, “Analysis of Taxi Travels during an Epidemic Period Using System Dynamics Method,” Sustainability, vol. 14, no. 6, Art. no. 6, Jan. 2022, doi: 10.3390/su14063457.
X. Wang, H. Zhang, and L. Wang, “A Demand-Supply Oriented Taxi Recommendation System for Vehicular Social Networks,” IEEE Access, vol. PP, pp. 1–1, Jul. 2018, doi: 10.1109/ACCESS.2018.2857002.
B. Hu, X. Xia, H. Sun, and X. Dong, “Understanding the imbalance of the taxi market: From the high-quality customer’s perspective,” Physica A: Statistical Mechanics and its Applications, vol. 535, p. 122297, Dec. 2019, doi: 10.1016/j.physa.2019.122297.
R. Ibrahim and M. O. Shafiq, “Detecting taxi movements using Random Swap clustering and sequential pattern mining,” J Big Data, vol. 6, no. 1, Art. no. 1, Dec. 2019, doi: 10.1186/s40537-019-0203-6.
Z. Ning, X. Wang, X. Kong, and W. Hou, “A Social-Aware Group Formation Framework for Information Diffusion in Narrowband Internet of Things,” IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1527–1538, Jun. 2018, doi: 10.1109/JIOT.2017.2777480.
Z. Zhou, J. Yu, Z. Guo, and Y. Liu, “Visual exploration of urban functions via spatio-temporal taxi OD data,” Journal of Visual Languages & Computing, vol. 48, pp. 169–177, Oct. 2018, doi: 10.1016/j.jvlc.2018.08.009.
I. P. S. Almantara, N. W. S. Aryani, and I. B. A. Swamardika, “Spatial Data Analysis using DBSCAN Method and KNN classification,” International Journal of Engineering and Emerging Technology, vol. 5, no. 2, pp. 77–80, Dec. 2020, doi: 10.24843/IJEET.2020.v05.i02.p013.
M. Amiruzzaman, R. Rahman, M. R. Islam, and R. M. Nor, “Logical analysis of built-in DBSCAN Functions in Popular Data Science Programming Languages.” OSF Preprints, Jun. 27, 2022. doi: 10.31219/osf.io/ge654.
“sklearn.metrics.pairwise.haversine_distances — scikit-learn 1.2.2 documentation.” Accessed: May 04, 2023. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html
S. T. I Made Suwija Putra, “ALGORITMA DBSCAN (DENSITY-BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE) DAN CONTOH PERHITUNGANNYA,” Jul. 2018, Accessed: Dec. 18, 2022. [Online]. Available: http://erepo.unud.ac.id/id/eprint/20097/
A. Devi, I. Putra, and I. Sukarsa, “Implementasi Metode Clustering DBSCAN pada Proses Pengambilan Keputusan,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, p. 185, Dec. 2015, doi: 10.24843/LKJITI.2015.v06.i03.p05.
P. Rousseeuw, M. Hubert, and A. Struyf, “Clustering in an Object-Oriented Environment,” Journal of Statistical Software, vol. 01, Feb. 1997, doi: 10.18637/jss.v001.i04.
D. Zhou, R. Hong, and J. Xia, Identification of Taxi Pick-Up and Drop-Off Hotspots Using the Density-Based Spatial Clustering Method. 2018, p. 204. doi: 10.1061/9780784480915.020.
Z. Huang, S. Gao, C. Cai, H. Zheng, Z. Pan, and W. Li, “A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+,” Sci Rep, vol. 11, no. 1, Art. no. 1, May 2021, doi: 10.1038/s41598-021-88822-3.
A. Rossi, G. Barlacchi, M. Bianchini, and B. Lepri, “Modelling Taxi Drivers’ Behaviour for the Next Destination Prediction,” undefined, 2020, doi: 10.1109/TITS.2019.2922002.
X. Wang, Y. Liu, Z. Liao, and Y. Zhao, “DeepFM-Based Taxi Pick-Up Area Recommendation,” in Pattern Recognition. ICPR International Workshops and Challenges, A. Del Bimbo, R. Cucchiara, S. Sclaroff, G. M. Farinella, T. Mei, M. Bertini, H. J. Escalante, and R. Vezzani, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2021, pp. 407–421. doi: 10.1007/978-3-030-68821-9_36.
Copyright (c) 2023 Mulia Dea Lestari, Lizda Iswari
This work is licensed under a Creative Commons Attribution 4.0 International License.